A matrix clustering method to explore patterns of land-cover transitions in satellite-derived maps of the Brazilian Amazon

Müller-Hansen, Finn; Cardoso, Manoel F.; Dalla-Nora, Eloi L.; Donges, Jonathan F.; Heitzig, Jobst; Kurths, Jürgen; Thonicke, Kirsten

doi:https://doi.org/10.5194/npg-24-113-2017

Articles | Volume 24, issue 1

https://doi.org/10.5194/npg-24-113-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/npg-24-113-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 24, issue 1

Research article

|

28 Feb 2017

Research article |

| 28 Feb 2017

A matrix clustering method to explore patterns of land-cover transitions in satellite-derived maps of the Brazilian Amazon

Finn Müller-Hansen, Manoel F. Cardoso, Eloi L. Dalla-Nora, Jonathan F. Donges, Jobst Heitzig, Jürgen Kurths, and Kirsten Thonicke

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Final revised paper (published on 28 Feb 2017)
Supplement to the final revised paper
Preprint (discussion started on 11 Oct 2016)
Supplement to the preprint

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review', Anastasios Tsonis, 21 Oct 2016
RC2: 'CR2', Anonymous Referee #2, 26 Dec 2016
AC1: 'Response to Referee Comments for the Manuscript “Patterns of land-cover transitions from satellite imagery of the Brazilian Amazon” by Finn Müller-Hansen et al.', Finn Müller-Hansen, 12 Jan 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Finn Müller-Hansen on behalf of the Authors (12 Jan 2017) Author's response Manuscript

ED: Publish as is (28 Jan 2017) by Stéphane Vannitsem

Short summary

Deforestation and subsequent land uses in the Brazilian Amazon have huge impacts on greenhouse gas emissions, local climate and biodiversity. To better understand these land-cover changes, we apply complex systems methods uncovering spatial patterns in regional transition probabilities between land-cover types, which we estimate using maps derived from satellite imagery. The results show clusters of similar land-cover dynamics and thus complement studies at the local scale.